{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   2024年      2023年        增长率\n",
      "项目                                            \n",
      "营业收入           356020000  795372000 -55.238555\n",
      "营业利润          -152576000 -465274000 -67.207280\n",
      "净利润           -151152000 -424324000 -64.378164\n",
      "经营活动产生的现金流量净额  151511000  112820000  34.294451\n",
      "\n",
      "新闻解读：\n",
      "- 营业收入增长率：-55.24%。这表明公司的收入能力有所下降，可能受到市场环境或公司战略调整的影响。\n",
      "- 营业利润增长率：-67.21%。营业利润的大幅下降可能暗示公司的成本控制或市场竞争力面临挑战。\n",
      "- 净利润增长率：-64.38%。净利润的减少可能影响投资者信心和公司的财务健康。\n",
      "- 经营活动产生的现金流量净额增长率：34.29%。现金流的增长表明公司的流动性有所改善，这是公司运营效率提升的积极信号。\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# 假设我们有一个DataFrame，包含皇庭国际企业股份有限公司的财务数据\n",
    "data = {\n",
    "    '项目': ['营业收入', '营业利润', '净利润', '经营活动产生的现金流量净额'],\n",
    "    '2024年': [356020000, -152576000, -151152000, 151511000],\n",
    "    '2023年': [795372000, -465274000, -424324000, 112820000]\n",
    "}\n",
    "\n",
    "df = pd.DataFrame(data).set_index('项目')\n",
    "\n",
    "# 计算增长率\n",
    "df['增长率'] = df.apply(lambda row: ((row['2024年'] - row['2023年']) / row['2023年']) * 100 if row['2023年'] != 0 else 0, axis=1)\n",
    "\n",
    "# 打印结果\n",
    "print(df)\n",
    "\n",
    "# 新闻解读\n",
    "def news_interpretation():\n",
    "    print(\"\\n新闻解读：\")\n",
    "    # 营业收入增长率\n",
    "    revenue_growth = df.loc['营业收入', '增长率']\n",
    "    print(f\"- 营业收入增长率：{revenue_growth:.2f}%。这表明公司的收入能力有所下降，可能受到市场环境或公司战略调整的影响。\")\n",
    "\n",
    "    # 营业利润增长率\n",
    "    profit_growth = df.loc['营业利润', '增长率']\n",
    "    print(f\"- 营业利润增长率：{profit_growth:.2f}%。营业利润的大幅下降可能暗示公司的成本控制或市场竞争力面临挑战。\")\n",
    "\n",
    "    # 净利润增长率\n",
    "    net_profit_growth = df.loc['净利润', '增长率']\n",
    "    print(f\"- 净利润增长率：{net_profit_growth:.2f}%。净利润的减少可能影响投资者信心和公司的财务健康。\")\n",
    "\n",
    "    # 现金流量增长率\n",
    "    cash_flow_growth = df.loc['经营活动产生的现金流量净额', '增长率']\n",
    "    print(f\"- 经营活动产生的现金流量净额增长率：{cash_flow_growth:.2f}%。现金流的增长表明公司的流动性有所改善，这是公司运营效率提升的积极信号。\")\n",
    "\n",
    "news_interpretation()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\陈嵩\\AppData\\Roaming\\Python\\Python313\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 33829 (\\N{CJK UNIFIED IDEOGRAPH-8425}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\陈嵩\\AppData\\Roaming\\Python\\Python313\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 19994 (\\N{CJK UNIFIED IDEOGRAPH-4E1A}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\陈嵩\\AppData\\Roaming\\Python\\Python313\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 25910 (\\N{CJK UNIFIED IDEOGRAPH-6536}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\陈嵩\\AppData\\Roaming\\Python\\Python313\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 20837 (\\N{CJK UNIFIED IDEOGRAPH-5165}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\陈嵩\\AppData\\Roaming\\Python\\Python313\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 21033 (\\N{CJK UNIFIED IDEOGRAPH-5229}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\陈嵩\\AppData\\Roaming\\Python\\Python313\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 28070 (\\N{CJK UNIFIED IDEOGRAPH-6DA6}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\陈嵩\\AppData\\Roaming\\Python\\Python313\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 20928 (\\N{CJK UNIFIED IDEOGRAPH-51C0}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\陈嵩\\AppData\\Roaming\\Python\\Python313\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 32463 (\\N{CJK UNIFIED IDEOGRAPH-7ECF}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\陈嵩\\AppData\\Roaming\\Python\\Python313\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 27963 (\\N{CJK UNIFIED IDEOGRAPH-6D3B}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\陈嵩\\AppData\\Roaming\\Python\\Python313\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 21160 (\\N{CJK UNIFIED IDEOGRAPH-52A8}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\陈嵩\\AppData\\Roaming\\Python\\Python313\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 20135 (\\N{CJK UNIFIED IDEOGRAPH-4EA7}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\陈嵩\\AppData\\Roaming\\Python\\Python313\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 29983 (\\N{CJK UNIFIED IDEOGRAPH-751F}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\陈嵩\\AppData\\Roaming\\Python\\Python313\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 30340 (\\N{CJK UNIFIED IDEOGRAPH-7684}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\陈嵩\\AppData\\Roaming\\Python\\Python313\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 29616 (\\N{CJK UNIFIED IDEOGRAPH-73B0}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\陈嵩\\AppData\\Roaming\\Python\\Python313\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 37329 (\\N{CJK UNIFIED IDEOGRAPH-91D1}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\陈嵩\\AppData\\Roaming\\Python\\Python313\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 27969 (\\N{CJK UNIFIED IDEOGRAPH-6D41}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\陈嵩\\AppData\\Roaming\\Python\\Python313\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 37327 (\\N{CJK UNIFIED IDEOGRAPH-91CF}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\陈嵩\\AppData\\Roaming\\Python\\Python313\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 39069 (\\N{CJK UNIFIED IDEOGRAPH-989D}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\陈嵩\\AppData\\Roaming\\Python\\Python313\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 39033 (\\N{CJK UNIFIED IDEOGRAPH-9879}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\陈嵩\\AppData\\Roaming\\Python\\Python313\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 30446 (\\N{CJK UNIFIED IDEOGRAPH-76EE}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\陈嵩\\AppData\\Roaming\\Python\\Python313\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 65288 (\\N{FULLWIDTH LEFT PARENTHESIS}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\陈嵩\\AppData\\Roaming\\Python\\Python313\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 20803 (\\N{CJK UNIFIED IDEOGRAPH-5143}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\陈嵩\\AppData\\Roaming\\Python\\Python313\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 65289 (\\N{FULLWIDTH RIGHT PARENTHESIS}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\陈嵩\\AppData\\Roaming\\Python\\Python313\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 20851 (\\N{CJK UNIFIED IDEOGRAPH-5173}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\陈嵩\\AppData\\Roaming\\Python\\Python313\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 38190 (\\N{CJK UNIFIED IDEOGRAPH-952E}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\陈嵩\\AppData\\Roaming\\Python\\Python313\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 36130 (\\N{CJK UNIFIED IDEOGRAPH-8D22}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\陈嵩\\AppData\\Roaming\\Python\\Python313\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 21153 (\\N{CJK UNIFIED IDEOGRAPH-52A1}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\陈嵩\\AppData\\Roaming\\Python\\Python313\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 25968 (\\N{CJK UNIFIED IDEOGRAPH-6570}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\陈嵩\\AppData\\Roaming\\Python\\Python313\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 25454 (\\N{CJK UNIFIED IDEOGRAPH-636E}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\陈嵩\\AppData\\Roaming\\Python\\Python313\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 21464 (\\N{CJK UNIFIED IDEOGRAPH-53D8}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\陈嵩\\AppData\\Roaming\\Python\\Python313\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 21270 (\\N{CJK UNIFIED IDEOGRAPH-5316}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\陈嵩\\AppData\\Roaming\\Python\\Python313\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 36235 (\\N{CJK UNIFIED IDEOGRAPH-8D8B}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\陈嵩\\AppData\\Roaming\\Python\\Python313\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 21183 (\\N{CJK UNIFIED IDEOGRAPH-52BF}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\陈嵩\\AppData\\Roaming\\Python\\Python313\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 24180 (\\N{CJK UNIFIED IDEOGRAPH-5E74}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\陈嵩\\AppData\\Roaming\\Python\\Python313\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 20221 (\\N{CJK UNIFIED IDEOGRAPH-4EFD}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\陈嵩\\AppData\\Local\\Temp\\ipykernel_8456\\1402628051.py:68: UserWarning: Glyph 33829 (\\N{CJK UNIFIED IDEOGRAPH-8425}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\陈嵩\\AppData\\Local\\Temp\\ipykernel_8456\\1402628051.py:68: UserWarning: Glyph 19994 (\\N{CJK UNIFIED IDEOGRAPH-4E1A}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\陈嵩\\AppData\\Local\\Temp\\ipykernel_8456\\1402628051.py:68: UserWarning: Glyph 25910 (\\N{CJK UNIFIED IDEOGRAPH-6536}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\陈嵩\\AppData\\Local\\Temp\\ipykernel_8456\\1402628051.py:68: UserWarning: Glyph 20837 (\\N{CJK UNIFIED IDEOGRAPH-5165}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\陈嵩\\AppData\\Local\\Temp\\ipykernel_8456\\1402628051.py:68: UserWarning: Glyph 19979 (\\N{CJK UNIFIED IDEOGRAPH-4E0B}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\陈嵩\\AppData\\Local\\Temp\\ipykernel_8456\\1402628051.py:68: UserWarning: Glyph 38477 (\\N{CJK UNIFIED IDEOGRAPH-964D}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\陈嵩\\AppData\\Local\\Temp\\ipykernel_8456\\1402628051.py:68: UserWarning: Glyph 21033 (\\N{CJK UNIFIED IDEOGRAPH-5229}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\陈嵩\\AppData\\Local\\Temp\\ipykernel_8456\\1402628051.py:68: UserWarning: Glyph 28070 (\\N{CJK UNIFIED IDEOGRAPH-6DA6}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\陈嵩\\AppData\\Local\\Temp\\ipykernel_8456\\1402628051.py:68: UserWarning: Glyph 20928 (\\N{CJK UNIFIED IDEOGRAPH-51C0}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\陈嵩\\AppData\\Local\\Temp\\ipykernel_8456\\1402628051.py:68: UserWarning: Glyph 29616 (\\N{CJK UNIFIED IDEOGRAPH-73B0}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\陈嵩\\AppData\\Local\\Temp\\ipykernel_8456\\1402628051.py:68: UserWarning: Glyph 37329 (\\N{CJK UNIFIED IDEOGRAPH-91D1}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\陈嵩\\AppData\\Local\\Temp\\ipykernel_8456\\1402628051.py:68: UserWarning: Glyph 27969 (\\N{CJK UNIFIED IDEOGRAPH-6D41}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\陈嵩\\AppData\\Local\\Temp\\ipykernel_8456\\1402628051.py:68: UserWarning: Glyph 22686 (\\N{CJK UNIFIED IDEOGRAPH-589E}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\陈嵩\\AppData\\Local\\Temp\\ipykernel_8456\\1402628051.py:68: UserWarning: Glyph 38271 (\\N{CJK UNIFIED IDEOGRAPH-957F}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\陈嵩\\AppData\\Local\\Temp\\ipykernel_8456\\1402628051.py:68: UserWarning: Glyph 20142 (\\N{CJK UNIFIED IDEOGRAPH-4EAE}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\陈嵩\\AppData\\Local\\Temp\\ipykernel_8456\\1402628051.py:68: UserWarning: Glyph 28857 (\\N{CJK UNIFIED IDEOGRAPH-70B9}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\陈嵩\\AppData\\Local\\Temp\\ipykernel_8456\\1402628051.py:68: UserWarning: Glyph 32489 (\\N{CJK UNIFIED IDEOGRAPH-7EE9}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\陈嵩\\AppData\\Local\\Temp\\ipykernel_8456\\1402628051.py:68: UserWarning: Glyph 24066 (\\N{CJK UNIFIED IDEOGRAPH-5E02}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\陈嵩\\AppData\\Local\\Temp\\ipykernel_8456\\1402628051.py:68: UserWarning: Glyph 22330 (\\N{CJK UNIFIED IDEOGRAPH-573A}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\陈嵩\\AppData\\Local\\Temp\\ipykernel_8456\\1402628051.py:68: UserWarning: Glyph 39118 (\\N{CJK UNIFIED IDEOGRAPH-98CE}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\陈嵩\\AppData\\Local\\Temp\\ipykernel_8456\\1402628051.py:68: UserWarning: Glyph 38505 (\\N{CJK UNIFIED IDEOGRAPH-9669}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\陈嵩\\AppData\\Local\\Temp\\ipykernel_8456\\1402628051.py:68: UserWarning: Glyph 36130 (\\N{CJK UNIFIED IDEOGRAPH-8D22}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\陈嵩\\AppData\\Local\\Temp\\ipykernel_8456\\1402628051.py:68: UserWarning: Glyph 21153 (\\N{CJK UNIFIED IDEOGRAPH-52A1}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\陈嵩\\AppData\\Local\\Temp\\ipykernel_8456\\1402628051.py:68: UserWarning: Glyph 27861 (\\N{CJK UNIFIED IDEOGRAPH-6CD5}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\陈嵩\\AppData\\Local\\Temp\\ipykernel_8456\\1402628051.py:68: UserWarning: Glyph 24459 (\\N{CJK UNIFIED IDEOGRAPH-5F8B}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\陈嵩\\AppData\\Local\\Temp\\ipykernel_8456\\1402628051.py:68: UserWarning: Glyph 35785 (\\N{CJK UNIFIED IDEOGRAPH-8BC9}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\陈嵩\\AppData\\Local\\Temp\\ipykernel_8456\\1402628051.py:68: UserWarning: Glyph 35772 (\\N{CJK UNIFIED IDEOGRAPH-8BBC}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\陈嵩\\AppData\\Local\\Temp\\ipykernel_8456\\1402628051.py:68: UserWarning: Glyph 25552 (\\N{CJK UNIFIED IDEOGRAPH-63D0}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\陈嵩\\AppData\\Local\\Temp\\ipykernel_8456\\1402628051.py:68: UserWarning: Glyph 31034 (\\N{CJK UNIFIED IDEOGRAPH-793A}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\陈嵩\\AppData\\Roaming\\Python\\Python313\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 33829 (\\N{CJK UNIFIED IDEOGRAPH-8425}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\陈嵩\\AppData\\Roaming\\Python\\Python313\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 19994 (\\N{CJK UNIFIED IDEOGRAPH-4E1A}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\陈嵩\\AppData\\Roaming\\Python\\Python313\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 25910 (\\N{CJK UNIFIED IDEOGRAPH-6536}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\陈嵩\\AppData\\Roaming\\Python\\Python313\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 20837 (\\N{CJK UNIFIED IDEOGRAPH-5165}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\陈嵩\\AppData\\Roaming\\Python\\Python313\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 19979 (\\N{CJK UNIFIED IDEOGRAPH-4E0B}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\陈嵩\\AppData\\Roaming\\Python\\Python313\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 38477 (\\N{CJK UNIFIED IDEOGRAPH-964D}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\陈嵩\\AppData\\Roaming\\Python\\Python313\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 21033 (\\N{CJK UNIFIED IDEOGRAPH-5229}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\陈嵩\\AppData\\Roaming\\Python\\Python313\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 28070 (\\N{CJK UNIFIED IDEOGRAPH-6DA6}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\陈嵩\\AppData\\Roaming\\Python\\Python313\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 20928 (\\N{CJK UNIFIED IDEOGRAPH-51C0}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\陈嵩\\AppData\\Roaming\\Python\\Python313\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 29616 (\\N{CJK UNIFIED IDEOGRAPH-73B0}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\陈嵩\\AppData\\Roaming\\Python\\Python313\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 37329 (\\N{CJK UNIFIED IDEOGRAPH-91D1}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\陈嵩\\AppData\\Roaming\\Python\\Python313\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 27969 (\\N{CJK UNIFIED IDEOGRAPH-6D41}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\陈嵩\\AppData\\Roaming\\Python\\Python313\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 22686 (\\N{CJK UNIFIED IDEOGRAPH-589E}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\陈嵩\\AppData\\Roaming\\Python\\Python313\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 38271 (\\N{CJK UNIFIED IDEOGRAPH-957F}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\陈嵩\\AppData\\Roaming\\Python\\Python313\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 20142 (\\N{CJK UNIFIED IDEOGRAPH-4EAE}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\陈嵩\\AppData\\Roaming\\Python\\Python313\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 28857 (\\N{CJK UNIFIED IDEOGRAPH-70B9}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\陈嵩\\AppData\\Roaming\\Python\\Python313\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 32489 (\\N{CJK UNIFIED IDEOGRAPH-7EE9}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\陈嵩\\AppData\\Roaming\\Python\\Python313\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 24066 (\\N{CJK UNIFIED IDEOGRAPH-5E02}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\陈嵩\\AppData\\Roaming\\Python\\Python313\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 22330 (\\N{CJK UNIFIED IDEOGRAPH-573A}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\陈嵩\\AppData\\Roaming\\Python\\Python313\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 39118 (\\N{CJK UNIFIED IDEOGRAPH-98CE}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\陈嵩\\AppData\\Roaming\\Python\\Python313\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 38505 (\\N{CJK UNIFIED IDEOGRAPH-9669}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\陈嵩\\AppData\\Roaming\\Python\\Python313\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 36130 (\\N{CJK UNIFIED IDEOGRAPH-8D22}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\陈嵩\\AppData\\Roaming\\Python\\Python313\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 21153 (\\N{CJK UNIFIED IDEOGRAPH-52A1}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\陈嵩\\AppData\\Roaming\\Python\\Python313\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 27861 (\\N{CJK UNIFIED IDEOGRAPH-6CD5}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\陈嵩\\AppData\\Roaming\\Python\\Python313\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 24459 (\\N{CJK UNIFIED IDEOGRAPH-5F8B}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\陈嵩\\AppData\\Roaming\\Python\\Python313\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 35785 (\\N{CJK UNIFIED IDEOGRAPH-8BC9}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\陈嵩\\AppData\\Roaming\\Python\\Python313\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 35772 (\\N{CJK UNIFIED IDEOGRAPH-8BBC}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\陈嵩\\AppData\\Roaming\\Python\\Python313\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 25552 (\\N{CJK UNIFIED IDEOGRAPH-63D0}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\陈嵩\\AppData\\Roaming\\Python\\Python313\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 31034 (\\N{CJK UNIFIED IDEOGRAPH-793A}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# 创建DataFrame\n",
    "data = {\n",
    "    '项目': ['营业收入', '营业利润', '净利润', '经营活动产生的现金流量净额'],\n",
    "    '2024年': [356020000, -152576000, -151152000, 151511000],\n",
    "    '2023年': [795372000, -465274000, -424324000, 112820000],\n",
    "    '增长率': [-55.24, -67.21, -64.38, 34.29]  # 以百分比形式给出\n",
    "}\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "# 绘制关键财务数据的变化趋势\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.set_style(\"whitegrid\")\n",
    "# 绘制2024年和2023年的数据\n",
    "for year in ['2024年', '2023年']:\n",
    "    sns.lineplot(x='项目', y=year, data=df, label=year)\n",
    "\n",
    "plt.title('关键财务数据变化趋势')\n",
    "plt.xlabel('项目')\n",
    "plt.ylabel('金额（元）')\n",
    "plt.xticks(rotation=45)\n",
    "plt.legend(title='年份')\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "# 使用信息图概括公司的业绩亮点和风险提示\n",
    "def create_infographic():\n",
    "    # 业绩亮点\n",
    "    highlights = pd.DataFrame({\n",
    "        '亮点': ['营业收入下降', '营业利润下降', '净利润下降', '现金流增长'],\n",
    "        '描述': [\n",
    "            '营业收入下降55.24%，可能受市场环境或战略调整影响。',\n",
    "            '营业利润下降67.21%，可能暗示成本控制或市场竞争力问题。',\n",
    "            '净利润下降64.38%，可能影响投资者信心和财务健康。',\n",
    "            '现金流增长34.29%，表明流动性改善，运营效率提升。'\n",
    "        ]\n",
    "    })\n",
    "    \n",
    "    # 风险提示\n",
    "    risks = pd.DataFrame({\n",
    "        '风险': ['市场风险', '财务风险', '法律诉讼风险'],\n",
    "        '描述': [\n",
    "            '由于营业收入和利润下降，公司面临市场风险。',\n",
    "            '高负债和负现金流可能引发财务风险。',\n",
    "            '多起法律诉讼可能对公司声誉和财务造成影响。'\n",
    "        ]\n",
    "    })\n",
    "    \n",
    "    # 绘制信息图\n",
    "    plt.figure(figsize=(12, 6))\n",
    "    sns.set_style(\"whitegrid\")\n",
    "    # 绘制业绩亮点\n",
    "    plt.subplot(1, 2, 1)\n",
    "    sns.countplot(x='亮点', data=highlights, order=highlights['亮点'].values)\n",
    "    plt.title('业绩亮点')\n",
    "    plt.xticks(rotation=45)\n",
    "    \n",
    "    # 绘制风险提示\n",
    "    plt.subplot(1, 2, 2)\n",
    "    sns.countplot(x='风险', data=risks, order=risks['风险'].values)\n",
    "    plt.title('风险提示')\n",
    "    plt.xticks(rotation=45)\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "create_infographic()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.0"
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 },
 "nbformat": 4,
 "nbformat_minor": 2
}
